Because mean performance levels across different cochlear implant systems are highly similar, and large outcome variation is observed across patients with the same device, it is hypothesized that physioanatomical factors play a significant role in determining outcome in individual cochlear implant patients. The long-term objective of this application is to determine how well the peripheral factors of (1) cochlear size and shape, (2) electrode insertion depth and scale tympani position and (3) neural survival and physiological responsiveness as measured in individual implant subjects can account for the performance variation seen across the same subjects. Factors related to cochlear anatomy and electrode placement can largely be measured by high-resolution CT imaging. Peripheral physiological responsiveness can be measured grossly using intracochlear-evoked potentials (IEP), whereas there are no direct in vivo measures of neural survival. This project will seek to estimate the combined influence of physiological responsiveness and variable neural survival in an individual subject by comparing IEP measures against a reference electro-anatomical-neural computational model that assumes full neural survival and uniform neural characteristics. The electro-anatomical reference model will be based however on the cochlear anatomy and electrode placement as determined by CT imaging in each individual subject. Multiple linear regression analysis will be used to examine the relative and combined contributions of each of these factors in accounting for variance in the subject performance outcomes. The immediate objective of this exploratory project is to construct and validate the necessary tools to make these measures and then conduct a small pilot study in 3-6 Nucleus and 3-6 Clarion implant subjects, hopefully leading to a subsequent large-scale study. Specifically, this study will seek to extend the fine spatial resolution of pre- and post-implantation high-resolution CT images using advanced preprocessing techniques before back projection, followed by subvoxel image interpolation and segmentation using shape-based interpolation kernels derived from micro-CT images of cadaveric temporal bones. This fine resolution CT image voxel-space will then be converted on a voxel-by-voxel basis into a finite-element-based electro anatomical neural model. Electrical fields and IEP responses will be computed for specific stimulation conditions and compared with measured IEP responses in a pilot study.